C. Ronald MacKenzie

Bio: C. Ronald MacKenzie is an academic researcher from Hospital for Special Surgery. The author has contributed to research in topics: Perioperative & Population. The author has an hindex of 19, co-authored 60 publications receiving 36116 citations. Previous affiliations of C. Ronald MacKenzie include Cornell College & NewYork–Presbyterian Hospital.

Why predictive indexes perform less well in validation studies. Is it magic or methods

Abstract: • When prognostic indexes have been tested in a second population, they have often performed less well. Since this is believed to be inevitable, methodologic differences that may explain the discrepancies have been overlooked. Data from a prospective study of 232 patients undergoing noncardiac surgery were used to examine the effect of methodologic differences in assembly of population, postoperative surveillance, and the criteria for cardiac complications on the performance of Goldman's cardiac risk index. Our prospective population was used to simulate the methods used in Goldman's study and in three other studies using the risk index to demonstrate the potential impact of differences in population, surveillance, and outcome criteria for cardiac complications. If Goldman's detection and outcome criteria were employed and only the eligibility criteria used for assembly of the populations differed, the overall complication rates would be between 5.2% and 6.9%; and the complication rates for the different Goldman classes were similar. When both different detection strategies and different outcome criteria were used, however, important discrepancies in cardiac complication rates emerged. For example, complication rates in class 2 varied from 2% to 23%. In conclusion, important discrepancies in performance of prognostic indexes may arise from differences in surveillance strategies and definitions of outcome. With sufficient attention to methodologic consistency, the performance of predictive indexes may not inevitably deteriorate in subsequent studies. ( Arch Intern Med 1987;147:2155-2161)

Comorbidity measures for use with administrative data.

Abstract: Objectives.This study attempts to develop a comprehensive set of comorbidity measures for use with large administrative inpatient datasets.Methods.The study involved clinical and empirical review of comorbidity measures, development of a framework that attempts to segregate comorbidities from other

Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data.

Abstract: Objectives:Implementation of the International Statistical Classification of Disease and Related Health Problems, 10th Revision (ICD-10) coding system presents challenges for using administrative data. Recognizing this, we conducted a multistep process to develop ICD-10 coding algorithms to define C

Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area.

Abstract: Importance There is limited information describing the presenting characteristics and outcomes of US patients requiring hospitalization for coronavirus disease 2019 (COVID-19). Objective To describe the clinical characteristics and outcomes of patients with COVID-19 hospitalized in a US health care system. Design, Setting, and Participants Case series of patients with COVID-19 admitted to 12 hospitals in New York City, Long Island, and Westchester County, New York, within the Northwell Health system. The study included all sequentially hospitalized patients between March 1, 2020, and April 4, 2020, inclusive of these dates. Exposures Confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection by positive result on polymerase chain reaction testing of a nasopharyngeal sample among patients requiring admission. Main Outcomes and Measures Clinical outcomes during hospitalization, such as invasive mechanical ventilation, kidney replacement therapy, and death. Demographics, baseline comorbidities, presenting vital signs, and test results were also collected. Results A total of 5700 patients were included (median age, 63 years [interquartile range {IQR}, 52-75; range, 0-107 years]; 39.7% female). The most common comorbidities were hypertension (3026; 56.6%), obesity (1737; 41.7%), and diabetes (1808; 33.8%). At triage, 30.7% of patients were febrile, 17.3% had a respiratory rate greater than 24 breaths/min, and 27.8% received supplemental oxygen. The rate of respiratory virus co-infection was 2.1%. Outcomes were assessed for 2634 patients who were discharged or had died at the study end point. During hospitalization, 373 patients (14.2%) (median age, 68 years [IQR, 56-78]; 33.5% female) were treated in the intensive care unit care, 320 (12.2%) received invasive mechanical ventilation, 81 (3.2%) were treated with kidney replacement therapy, and 553 (21%) died. As of April 4, 2020, for patients requiring mechanical ventilation (n = 1151, 20.2%), 38 (3.3%) were discharged alive, 282 (24.5%) died, and 831 (72.2%) remained in hospital. The median postdischarge follow-up time was 4.4 days (IQR, 2.2-9.3). A total of 45 patients (2.2%) were readmitted during the study period. The median time to readmission was 3 days (IQR, 1.0-4.5) for readmitted patients. Among the 3066 patients who remained hospitalized at the final study follow-up date (median age, 65 years [IQR, 54-75]), the median follow-up at time of censoring was 4.5 days (IQR, 2.4-8.1). Conclusions and Relevance This case series provides characteristics and early outcomes of sequentially hospitalized patients with confirmed COVID-19 in the New York City area.